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我遇到了這個奇怪的問題。Python csv切斷部分列
我還應該提到這在過去有效，所以我也在考慮可能是.csv或特定行本身有問題。
快速分解。我有一個腳本從CVE（漏洞）數據的.csv文件中提取數據。然後，它使用cvss模塊來重新調整我們使用輸出的結果，以此來衡量修補和緊急度的優先級。
（這個腳本是一個臨時的解決辦法，直到我們實現新的工具）
這裏是它攪亂了。這是我的攝取文件輸出現在看起來像。
Vulnerability Title,Plugin ID,Original CVSS Score,Default Vector,Original Severity,AWS Score,AWS Vector,AWS Severity,Hosts,Host Type,Percentage Impacted
Cisco IOS IKEv1 Packet Handling Remote Information Disclosure (cisco-sa-20160916-ikev1) (BENIGNCERTAIN),NES-93736,4.6,CVSS2#AV:N/AC:L/Au:N/C:P/I:N/A:N,,,AV:N/AC:L/Au:N/C:P/I:N/A:N,,26,26,
Cisco IOS Software TCP Memory Leak DoS (cisco-sa-20150325-tcpleak),NES-82568,4.9,CVSS2#AV:N/AC:L/Au:N/C:N/I:N/A:C,,,AV:N/AC:L/Au:N/C:N/I:N/A:C,,30,26,
RHEL 5/6/7 : nss and nss-util (RHSA-2016:2779),NES-94912,4.2,CVSS2#AV:N/AC:M/Au:N/C:C/I:C/A:C/E:F/RL:OF/RC:ND,,,AV:N/AC:M/Au:N/C:C/I:C/A:C/E:F/RL:OF/RC:ND,,5112,23,
這裏是我的腳本後的輸出（其中波紋管附後）
Vulnerability Title,Plugin ID,Original CVSS Score,Default Vector,Original Severity,AWS Score,AWS Vector,AWS Severity,Hosts,Host Type,Percentage Impacted
ium,4.6,AV:A/AC:H/Au:M/C:P/I:N/A:P/CDP:L/TD:H/CR:H/IR:H/AR:H,Medium,26,26,0.2524271844660194
Cisco IOS Software TCP Memory Leak DoS (cisco-sa-20150325-tcpleak),NES-82568,4.9,CVSS2#AV:N/AC:L/Au:N/C:N/I:N/A:C,Medium,4.9,AV:A/AC:H/Au:M/C:N/I:N/A:C/CDP:L/TD:M/CR:H/IR:H/AR:H,Medium,30,26,0.2912621359223301
RHEL 5/6/7 : nss and nss-util (RHSA-2016:2779),NES-94912,4.2,CVSS2#AV:N/AC:M/Au:N/C:C/I:C/A:C/E:F/RL:OF/RC:ND,Medium,4.2,AV:A/AC:H/Au:M/C:C/I:C/A:C/E:F/RL:OF/RC:ND/CDP:L/TD:M/CR:H/IR:H/AR:H,Medium,5112,23,0.615458704550927
要一點點進一步解釋，一號線與「IUM」是字詞介質的切斷開始其來自我的腳本的底部（第128行）（#ORIGINAL SCORE部分）。它應該說中等。所以基本上，如果你看起來像我的輸入2，並與輸出進行比較，它將切出整行，並且只添加腳本正在嘗試添加的一半字。我想也許是因爲所有的括號或者什麼，但我不確定。
Cisco IOS IKEv1 Packet Handling Remote Information Disclosure (cisco-sa-20160916-ikev1) (BENIGNCERTAIN),NES-93736,4.6,CVSS2#AV:N/AC:L/Au:N/C:P/I:N/A:N,
這是執行此功能的腳本。我知道它有點難看，並且歡迎提出改進建議，但要知道爲什麼它搞亂我的文件是我現在的首要任務。我曾考慮轉用熊貓，但這需要一些時間，因爲我從來沒有使用它，所以不知道如何做到這一點。
def rescore_function():
#headers
print 'Starting Rescore'
csv_in = open('/tmp/rescore_test.csv', 'rb')
csv_out = open('/tmp/rescored_vulnerabilities.csv', 'wb')
writer = csv.writer(csv_out)
reader = csv.reader(csv_in)
headers = next(reader, None)
if headers:
writer.writerow(headers)
print 'Creating Target Distrobution'
for row in csv.reader(csv_in):
#This is a terrible way of setting up the percentage of hosts impacted for target distrobution. Its ugly and horrible. Host count defines the host impacted, host_type identifies what kind of host it is. Such as Alinux, Rhel5, or Cisco IOS
host_count = float(row[8])
host_type = float(row[9])
alinux_impact = host_count/ALINUX_HOST
cisco_impact = host_count/CISCO_COUNT
juniper_impact = host_count/JUNIPER_COUNT
citrix_impact = host_count/CITRIX_COUNT
all_linux= host_count/LINUX_TOTAL
print 'math set'
#The reason for vul_id is 3 lists combined is simple. alinux_impact NEEDS to be 24, cisco NEEDs to be 26, juniper NEEDS to match 27, because vul_id is the softwares 'vulnerability ID type
#range falls into all_linux. So fillvalue=vul_os[-1] means if its not 24,26,27, it is "all_linux" which means it compares it to the All linux number.
vul_id = [24, 26, 27, 25] + range(24) + range(28,101)
vul_os = [alinux_impact, cisco_impact, juniper_impact, all_linux]
append_file = open('/tmp/rescored_vulnerabilities.csv', 'ab')
append_write = csv.writer(append_file)
#Does the for loop with the fillvalue as mentioned above. Basically Y is the host type (linux, Cisco IOS, etc) and X is the vulnerability type. So it runs through and figures out the TD and rescore methods.
#X equals the percetange of impacted, so the Metric will be based on amount/percentage of X impacted and does a regex search and replace based on that using the CVSS calculations.
print vul_id
print vul_os
for x,y in izip_longest(vul_os, vul_id, fillvalue=vul_os[-1]):
print x,y
print host_type
#VECTOR REGEXP, host_type is which OS/Device type. 23 = RHEL5, 24 = Alinux, 26 = Cisco, 27 = Juniper
if host_type == y:
row[10] = x
if x <= 0.25:
AC_Metric = 'A:C/CDP:L/TD:L/CR:H/IR:H/AR:H'
AP_Metric = 'A:P/CDP:L/TD:L/CR:H/IR:H/AR:H'
AN_Metric = 'A:N/CDP:L/TD:L/CR:H/IR:H/AR:H'
RCUC_Metric = 'RC:UC/CDP:L/TD:L/CR:H/IR:H/AR:H'
RCUR_Metric = 'RC:UR/CDP:L/TD:L/CR:H/IR:H/AR:H'
RCC_Metric = 'RC:C/CDP:L/TD:L/CR:H/IR:H/AR:H'
RCND_Metric = 'RC:ND/CDP:L/TD:L/CR:H/IR:H/AR:H'
elif 0.26 <= x <= 0.75:
AC_Metric = 'A:C/CDP:L/TD:M/CR:H/IR:H/AR:H'
AP_Metric = 'A:P/CDP:L/TD:M/CR:H/IR:H/AR:H'
AN_Metric = 'A:N/CDP:L/TD:M/CR:H/IR:H/AR:H'
RCUC_Metric = 'RC:UC/CDP:L/TD:M/CR:H/IR:H/AR:H'
RCUR_Metric = 'RC:UR/CDP:L/TD:M/CR:H/IR:H/AR:H'
RCC_Metric = 'RC:C/CDP:L/TD:M/CR:H/IR:H/AR:H'
RCND_Metric = 'RC:ND/CDP:L/TD:M/CR:H/IR:H/AR:H'
else:
AC_Metric = 'A:C/CDP:L/TD:H/CR:H/IR:H/AR:H'
AP_Metric = 'A:P/CDP:L/TD:H/CR:H/IR:H/AR:H'
AN_Metric = 'A:N/CDP:L/TD:H/CR:H/IR:H/AR:H'
RCUC_Metric = 'RC:UC/CDP:L/TD:H/CR:H/IR:H/AR:H'
RCUR_Metric = 'RC:UR/CDP:L/TD:H/CR:H/IR:H/AR:H'
RCC_Metric = 'RC:C/CDP:L/TD:H/CR:H/IR:H/AR:H'
RCND_Metric = 'RC:ND/CDP:L/TD:H/CR:H/IR:H/AR:H'
text = row[6]
text = re.sub(r'AV:N','AV:A',text)
text = re.sub(r'AC:L','AC:H',text)
text = re.sub(r'AC:M','AC:H',text)
text = re.sub(r'Au:N','Au:M',text)
text = re.sub(r'Au:S','Au:M',text)
text = re.sub(r'A:C$',AC_Metric,text)
text = re.sub(r'A:P$',AP_Metric,text)
text = re.sub(r'A:N$',AP_Metric,text)
text = re.sub(r'RC:UC',RCUC_Metric,text)
text = re.sub(r'RC:UR',RCUR_Metric,text)
text = re.sub(r'RC:C',RCC_Metric,text)
text = re.sub(r'RC:ND',RCND_Metric,text)
row[6] = text
#NEW SCORE, uses CVSS module to take the previous vector and find out the the numbered score. It then uses that number to define the severity word.
try:
vector = row[6]
c = CVSS2(vector)
row[5] = c.scores()[2]
vul_score = row[5]
if 0 <= vul_score <= 3.9:
vuln_word = 'Low'
elif 4.0 <= vul_score <=6.9:
vuln_word = 'Medium'
elif 7.0 <= vul_score <= 9.9:
vuln_word = 'High'
else:
vuln_word = 'Critical'
row[7] = vuln_word
except CVSS2MalformedError:
rescored_success = False
pass
#ORIGINAL SCORE, does the same as above for the original vector since NESSUS does not provide the Severity "word". This only finds the word, not the number value.
default_score = float(row[2])
if 0 <= default_score <= 3.9:
default_severity = 'Low'
elif 4.0 <= default_score <=6.9:
default_severity = 'Medium'
elif 7.0 <= default_score <= 9.9:
default_severity = 'High'
else:
default_severity = 'Critical'
row[4] = default_severity
append_write.writerow(row)
你爲什麼用'rb'模式閱讀？這不是一個二進制文件，是嗎？用'r'嘗試。 – jbasko
@jbasko'rb'是python2中csv.reader的推薦模式（https://docs.python.org/2/library/csv.html#module-contents） – snakecharmerb
謝謝@snakecharmerb不知道。 – jbasko